Adaptive Advanced Driving Assistance System (ADAS)

Abstract

Techniques are disclosed for implementing an adaptive vehicle Advanced Driving Assistance System (ADAS) unit, also referred to as a "Intelligent" ADAS. The intelligent ADAS unit sends a vehicle ADAS message that is received and aggregated by the remote computing system. The remote computing system may optionally include supplemental data, such as weather information, traffic data, etc., in an aggregated data set. The remote computing system identifies ADAS alarm events and their corresponding locations from the aggregated dataset and uses a predetermined set of rules to identify potential ADAS alarm configuration settings that can be updated by vehicles in service range. The ADAS configuration message provides instructions for each vehicle as to whether, when, and how the ADAS configuration settings should be adjusted, which may include adjustments to ADAS alarm sensitivity settings to dynamically adjust the manner in which each ADAS alarm event issues an ADAS alarm.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to Provisional Application No. 63,326,072 filed on March 31, 2022, the contents of which are incorporated herein by reference in their entirety. Technical Field

[0003] The present disclosure relates generally to advanced driver assistance systems (ADAS), and more particularly, to the implementation of adaptive (ie, "smart") ADAS (SADAS). Background Art

[0004] The function of an Advanced Driver Assistance System (ADAS) unit is to identify objects on the road, including people, signs, and light sources, to keep passengers and surrounding road users and road infrastructure safe. To this end, the ADAS unit uses various vehicle sensors to identify ADAS alert events based on detected objects, environmental conditions, etc., and then generates ADAS alerts to the occupants of the vehicle when specific conditions are met, which are defined by the ADAS alert sensitivity settings of the ADAS unit. However, a limitation of conventional ADAS units is that their ability to detect ADAS alert events is typically limited by the sensor range of the vehicle sensors. In addition, conventional ADAS units handle each ADAS alert event in the same manner, i.e., by applying the same ADAS alert setting to each event, regardless of other conditions that may increase the severity of the ADAS alert event when present. Therefore, current ADAS units are unable to dynamically adapt to changes in the vehicle environment and are insufficient to enhance the safety of vehicle occupants. BRIEF DESCRIPTION OF THE DRAWINGS

[0005] The accompanying drawings, which are incorporated herein and form a part of the specification, illustrate aspects of the present disclosure and, together with the description, further serve to explain the principles of the aspects and to enable one skilled in the relevant art to make and use the aspects.

[0006] Figure 1 An example vehicle is presented in accordance with one or more aspects of the present disclosure.

[0007] Figure 2 Various example electronic components of a safety system of a vehicle according to one or more aspects of the present disclosure are presented;

[0008] Figure 3 An example architecture for implementing a smart ADAS (SADAS) alert according to one or more aspects of the present disclosure is presented;

[0009] FIG. 4A to FIG. 4B An example rule set for implementing a SADAS alert in accordance with one or more aspects of the present disclosure is presented;

[0010] Figure 5 An example road scenario for selectively adjusting ADAS alert sensitivity settings according to one or more aspects of the present disclosure is presented;

[0011] Figure 6 An example SADAS icon set according to one or more aspects of the present disclosure is shown; and

[0012] Figure 7 An example process flow according to one or more aspects of the present disclosure is presented.

[0013] Exemplary aspects of the disclosure will be described with reference to the accompanying drawings. The drawing in which an element first appears is generally indicated by the leftmost digit(s) in the corresponding reference number. DETAILED DESCRIPTION

[0014] In the following description, numerous specific details are set forth in order to provide a thorough understanding of various aspects of the present disclosure. However, it will be apparent to those skilled in the art that various aspects, including structures, systems, and methods, can be practiced without these specific details. The descriptions and representations herein are common means by which experienced persons or technicians in the art are used to most effectively convey the essence of their work to other technicians in the art. In other instances, well-known methods, procedures, components, and circuits are not described in detail to avoid unnecessarily obscuring various aspects of the present disclosure.

[0015] Autonomous Vehicle Architecture and Operations

[0016] Figure 1 A security system 200 (see also FIG. Figure 2 ) of a vehicle 100. The vehicle 100 and safety system 200 are exemplary in nature and, therefore, may be simplified for purposes of explanation. The orientation of elements and relative distances (as discussed herein, the drawings are not to scale) are provided by way of example and not limitation. The safety system 200 may include various components (depending on the requirements of the particular implementation and / or application) and may facilitate navigation and / or control of the vehicle 100. The vehicle 100 may be an autonomous vehicle (AV), which may include any level of automation (e.g., levels 0 to 5), including no automation or full automation (level 5). The vehicle 100 may implement the safety system 200 as part of any suitable type of autonomous or driver-assisted control system (e.g., including AV and / or advanced driver assistance systems (ADAS)). The safety system 200 may include one or more components that are integrated as part of the vehicle 100 during manufacturing, as part of an add-on or aftermarket device, or a combination of these. Thus, as Figure 2The various components of the safety system 200 shown may be integrated as part of the vehicle's system and / or part of an aftermarket system installed in the vehicle 100 .

[0017] The one or more processors 102 may be integrated or separate from an electronic control unit (ECU) of the vehicle 100 or an engine control unit of the vehicle 100 (which may be considered a dedicated type of electronic control unit herein). The safety system 200 may generate data to control or assist in controlling the ECU and / or other components of the vehicle 100 to directly or indirectly control the driving of the vehicle 100. However, the aspects described herein are not limited to implementation within autonomous or semi-autonomous vehicles, as these are provided by way of example. The aspects described herein may be implemented as part of any suitable type of vehicle capable of traveling in a particular driving environment with or without any suitable level of human assistance. Thus, in various aspects, various vehicle components (e.g., such as those described herein with reference to Figure 2 One or more of the safety system 200 discussed above) may be implemented as part of a standard vehicle (i.e., a vehicle not utilizing autonomous driving functionality), a fully autonomous vehicle, and / or a semi-autonomous vehicle. In aspects implemented as part of a standard vehicle, it should be understood that the safety system 200 may perform alternative functionality, and thus, according to such aspects, the safety system 200 may alternatively represent any suitable type of system that may be implemented by a standard vehicle without necessarily utilizing autonomous or semi-autonomous control related functionality.

[0018] Regardless of Figure 1 and Figure 2 Regardless of the particular embodiment of the vehicle 100 and accompanying safety system 200 illustrated, the safety system 200 may include one or more processors 102, one or more image acquisition devices 104 (such as, for example, one or more vehicle cameras or any other suitable sensor configured to perform image acquisition within any suitable wavelength range), one or more position sensors 106 (which may be implemented as a position and / or orientation identification system, such as a global navigation satellite system (GNSS), for example, a global positioning system (GPS)), one or more memories 202, one or more map databases 204, one or more user interfaces 206 (such as, for example, a display, a touch screen, a microphone, a speaker, one or more buttons and / or switches, etc.), and one or more wireless transceivers 208, 210, 212. Additionally or alternatively, as further discussed herein, the one or more user interfaces 206 may be identified with other components in communication with the safety system 200, such as one or more components of an ADAS unit, an AV system, etc.

[0019] The wireless transceivers 208, 210, 212 may be configured to operate according to any suitable number and / or type of desired radio communication protocols or standards. For example, a wireless transceiver (e.g., the first wireless transceiver 208) may be configured according to a short-range mobile radio communication standard, such as, for example, Bluetooth, ZigBee, etc. As another example, a wireless transceiver (e.g., the second wireless transceiver 210) may be configured according to a medium-range or wide-range mobile radio communication standard, such as, for example, a 3G (e.g., Universal Mobile Telecommunications System - UMTS), 4G (e.g., Long Term Evolution - LTE) or 5G mobile radio communication standard according to the corresponding 3GPP (Third Generation Partnership Project) standard (the latest version at the time of writing is 3GPP Release 16 (2020)).

[0020] As yet another example, the wireless transceiver (e.g., the third wireless transceiver 212) may be configured according to a wireless local area network communication protocol or standard, such as, for example, according to the IEEE 802.11 Working Group standard, the latest version of which at the time of writing is IEEE Std 802.11 published on February 26, 2021. TM -2020 (e.g., 802.11, 802.11a, 802.11b, 802.11g, 802.11n, 802.11p, 802.11-12, 802.11ac, 802.11ad, 802.11ah, 802.11ax, 802.11ay, etc.). One or more wireless transceivers 208, 210, 212 may be configured to transmit signals via an antenna system (not shown) using an air interface. As an additional example, one or more of the transceivers 208, 210, 212 may be configured to implement one or more vehicle-to-everything (V2X) communication protocols, which may include vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-network (V2N), vehicle-to-pedestrian (V2P), vehicle-to-device (V2D), vehicle-to-grid (V2G), and any other suitable communication protocol.

[0021] One or more of the wireless transceivers 208, 210, 212 may additionally or alternatively be configured to enable communication between the vehicle 100 and one or more other remote computing devices via one or more wireless links 140. For example, this may include communicating with a remote server or other such as a Figure 1 Communications with other suitable computing systems 150 as shown. Figure 1The illustrated example shows such remote computing system 150 as a cloud computing system, but this is by way of example and not limitation, and computing system 150 may be implemented according to any suitable architecture and / or network, and may constitute one or more physical computers, servers, processors, etc. comprising such a system. As another example, remote computing system 150 may be implemented as an edge computing system and / or network.

[0022] The one or more processors 102 may implement any suitable type of processing circuitry, other suitable circuitry, memory, etc., and use any suitable type of architecture. The one or more processors 102 may be configured as a controller implemented by the vehicle 100 to perform various vehicle control functions, navigation functions, etc. For example, the one or more processors 102 may be configured to act as a controller for the vehicle 100 to analyze sensor data and received communications, calculate specific actions for the vehicle 100 to perform navigation and / or control of the vehicle 100, and cause corresponding actions to be performed, which may be based on, for example, an AV or ADAS system. The one or more processors 102 and / or the safety system 200 may form the entirety or a portion of an advanced driver assistance system (ADAS), and as further discussed herein, may form part of a "smart" ADAS unit that provides additional functionality and features.

[0023] In addition, one or more of the processors 214A, 214B, 216, and / or 218 in the one or more processors 102 may be configured to operate in cooperation with each other and / or with other components of the vehicle 100 to collect information about the environment (e.g., sensor data, such as images, depth information (for example, lidar), etc.). In this context, one or more of the processors 214A, 214B, 216, and / or 218 in the one or more processors 102 may be referred to as a "processor." Thus, the processors may be implemented (independently or together) to create map information from the collected data, such as road segment data (RSD) information that may be used for road experience management (REM) mapping technology, details of which are further described below. As another example, the processor may be implemented to process map information (e.g., road book information for REM mapping technology) received from a remote server via a wireless communication link (e.g., link 140) to locate the vehicle 100 on an AV map, which map information may be used by the processor to control the vehicle 100.

[0024] The one or more processors 102 may include one or more application processors 214A, 214B, an image processor 216, a communication processor 218, and may additionally or alternatively include any other suitable processing devices, circuits, components, etc. that are not shown in the drawings for the sake of simplicity. Similarly, the image acquisition device 104 may include any suitable number of image acquisition devices and components, depending on the requirements of a particular application. The image acquisition device 104 may include one or more image capture devices (e.g., a camera, a charge coupled device (CCD), or any other type of image sensor). The security system 200 may also include a data interface that communicatively connects the one or more processors 102 to the one or more image acquisition devices 104. For example, the first data interface may include any wired and / or wireless first link 220 or multiple wired and / or wireless first links 220 for sending image data acquired by the one or more image acquisition devices 104 to the one or more processors 102, for example, to the image processor 216.

[0025] The wireless transceivers 208, 210, 212 may be coupled to the one or more processors 102, e.g., to the communication processor 218, e.g., via a second data interface. The second data interface may include any wired and / or wireless second link 222 or multiple wired and / or wireless second links 222 for transmitting radio transmission data acquired by the wireless transceivers 208, 210, 212 to the one or more processors 102, e.g., to the communication processor 218. Such transmissions may also include (one-way or two-way) communications between the vehicle 100 and one or more other (target) vehicles in the environment of the vehicle 100 (e.g., to facilitate coordination of navigation of the vehicle 100 based on or with other (target) vehicles in the environment of the vehicle 100), or even broadcast transmissions directed to unspecified recipients in the vicinity of the transmitting vehicle 100.

[0026] The memory 202 and the one or more user interfaces 206 may be coupled to each of the one or more processors 102, for example, via a third data interface. The third data interface may include any wired and / or wireless third link 224 or a plurality of any wired and / or wireless third links 224. Furthermore, the position sensor 106 may be coupled to each of the one or more processors 102, for example, via a third data interface.

[0027] Each processor 214A, 214B, 216, 218 in the one or more processors 102 may be implemented as any suitable number and / or type of hardware-based processing devices (e.g., processing circuits), and may collectively, i.e., together with the one or more processors 102, form one or more types of controllers as discussed herein. Figure 2The architecture shown is for ease of explanation and as an example, and the vehicle 100 may include any suitable number of one or more processors 102 , each of which may be similarly configured to utilize data received via various interfaces and perform one or more specific tasks.

[0028] For example, one or more processors 102 may form a controller configured to perform various control-related functions of the vehicle 100, such as calculation and execution of a particular vehicle following speed, velocity, acceleration, braking, steering, trajectory, etc. As another example, in addition to or in lieu of one or more processors 102, the vehicle 100 may implement other processors (not shown) that may form different types of controllers configured to perform additional or alternative types of control-related functions. Each controller may be responsible for controlling a particular subsystem and / or control associated with the vehicle 100. According to such aspects, each controller may be connected to the vehicle 100 via a corresponding interface (e.g., 220, 222, 224, 232, etc.) from a plurality of controllers such as the vehicle 100. Figure 2 The respective coupled components shown receive data, with the wireless transceivers 208, 210 and / or 212 providing data to the respective controllers via a second link 222, which in this example serves as a communication interface between the respective wireless transceivers 208, 210 and / or 212 and each respective controller.

[0029] To provide another example, application processors 214A, 214B may individually represent respective controllers that work together with one or more processors 102 to perform specific control-related tasks. For example, application processor 214A may be implemented as a first controller, while application processor 214B may be implemented as a second and different type of controller configured to perform other types of tasks as further discussed herein. According to such aspects, one or more processors 102 may receive and receive data from the processors 102 via various interfaces 220, 222, 224, 232, etc. Figure 2 The corresponding coupled components shown receive data, and the communication processor 218 can provide communication data received from (or to be sent to) other vehicles to each controller via the corresponding coupled links 240A, 240B, which in this example serve as a communication interface between the corresponding application processors 214A, 214B and the communication processor 218. Of course, in addition to or in lieu of control-based functions, the application processors 214A, 214B can perform other functions, such as the various processing functions discussed herein, providing ADAS alerts, providing warnings about possible collisions, etc.

[0030] The one or more processors 102 may additionally be implemented to communicate with any other suitable components of the vehicle 100 to determine the state of the vehicle while driving or at any other suitable time, which may include analysis of data representing the state of the vehicle. For example, the vehicle 100 may include one or more vehicle computers, sensors, ECUs, interfaces, etc., which may be collectively referred to as vehicle components 230, such as Figure 2 As shown. The one or more processors 102 are configured to communicate with the vehicle component 230 via an additional data interface 232, which may represent any suitable type of link and operate according to any suitable communication protocol (e.g., CAN bus communication). Using the data received via the data interface 232, the one or more processors 102 may determine any suitable type of vehicle condition information, such as the current driving gear of the vehicle 100, the current engine speed, the acceleration capability, etc. As another example, various metrics for controlling speed, acceleration, braking, steering, etc. may be received via the vehicle component 230, which may include receiving any suitable type of signal indicating different degrees of such metrics or how such metrics change over time (e.g., braking force, wheel angle, reverse gear, etc.).

[0031] The one or more processors 102 may include any suitable number of other processors 214A, 214B, 216, 218, each of which may include processing circuits such as subprocessors, microprocessors, preprocessors (such as image preprocessors), graphics processors, central processing units (CPUs), support circuits, digital signal processors, integrated circuits, memory, or any other type of device suitable for running applications and performing data processing (e.g., image processing, audio processing, etc.) and analysis and / or enabling vehicle control to be functionally implemented. In some aspects, each processor 214A, 214B, 216, 218 may include any suitable type of single-core or multi-core processor, microcontroller, central processing unit, etc. These processor types may each include multiple processing units with local memory and instruction sets. Such processors may include video inputs for receiving image data from multiple image sensors, and may also include video output capabilities.

[0032] Any of the processors 214A, 214B, 216, 218 disclosed herein may be configured to perform certain functions according to program instructions, which may be stored in a local memory of each respective processor 214A, 214B, 216, 218, or accessed via another memory that is part of the security system 200 or external to the security system 200. This memory may include one or more memories 202. Regardless of the particular type and orientation of the memory, the memory may store software and / or executable (i.e., computer-readable) instructions that, when executed by an associated processor (e.g., by one or more processors 102, one or more of the processors 214A, 214B, 216, 218, etc.), control the operation of the security system 200, and may perform other functions, such as those associated with any of the aspects described in further detail below. As one example, one or more processors 102 , which may include one or more of processors 214A, 214B, 216 , 218 , etc., may execute computer-readable instructions to perform one or more intelligent ADAS functions as discussed herein.

[0033] The associated memory (e.g., one or more memories 202) accessed by one or more processors 214A, 214B, 216, 218 may also store one or more databases and image processing software and trained systems (such as neural networks, or deep neural networks, for example) that can be used to perform tasks according to any aspect as discussed herein. The associated memory (e.g., one or more memories 202) accessed by one or more processors 214A, 214B, 216, 218 may be implemented as any suitable number and / or type of non-transitory computer-readable media, such as random access memory, read-only memory, flash memory, disk drives, optical storage devices, tape storage devices, removable storage devices, or any other suitable type of storage device.

[0034] Display and Figure 2 The components associated with the security system 200 are shown for ease of explanation and are provided as examples rather than limitations. The security system 200 may include additional, fewer, or alternative components, as described herein. Figure 2 Additionally, one or more components of the security system 200 may be integrated or otherwise combined into Figure 2The common processing circuit components shown or separated from those common processing circuit components to form unique and independent components. For example, one or more of the components of the safety system 200 can be integrated with each other on a common bare die or chip. As an illustrative example, one or more processors 102 and related memories (e.g., one or more memories 202) accessed by one or more processors 214A, 214B, 216, 218 can be integrated on a common chip, bare die, package, etc., and together include a controller or system configured to perform one or more specific tasks or functions. Again, such a controller or system can be configured as an ADAS unit, which is configured to perform functions related to determining whether to adjust the ADAS alarm sensitivity configuration, when to present an ADAS alarm notification, etc., as discussed in further detail herein, to present relevant warnings and / or control the state of the vehicle 100 in which the safety system 200 is implemented.

[0035] In some aspects, the safety system 200 may further include a component for measuring the speed of the vehicle 100, such as a speed sensor 108 (e.g., a speedometer). The safety system 200 may also include one or more inertial measurement unit (IMU) sensors, such as, for example, an accelerometer, a magnetometer, and / or a gyroscope (single or multiple axes) for measuring the acceleration of the vehicle 100 along one or more axes, and additionally or alternatively one or more gyroscope sensors, which may be implemented alone or in combination with other suitable vehicle sensors, for example to calculate the ego motion of the vehicle, as discussed herein. For example, these IMU sensors may be part of the position sensor 105, as discussed herein. The safety system 200 may further include additional sensors or different sensor types, such as ultrasonic sensors, thermal sensors, one or more radar sensors 110, one or more LIDAR sensors 112 (which may be integrated in the headlights of the vehicle 100), a digital compass, and the like. The radar sensor 110 and / or the LIDAR sensor 112 may be configured to provide pre-processed sensor data, such as a radar target list or a LIDAR target list. A third data interface (eg, one or more links 224 ) may couple the speed sensor 108 , the one or more radar sensors 110 , and the one or more LIDAR sensors 112 to at least one of the one or more processors 102 .

[0036] Autonomous Vehicle (AV) Map Data and Road Experience Management (REM)

[0037] The data referred to as REM map data (or alternatively, road book map data) may also be stored in an associated memory (e.g., memory(s) 202) accessed by the one or more processors 214A, 214B, 216, 218 or in any suitable location and / or format (such as in a local or cloud-based database), accessed via communication between the vehicle and one or more external components (e.g., via transceivers 208, 210, 212), etc. It should be noted that although referred to herein as "AV map data," the data may be implemented in any suitable vehicle platform, which may include vehicles having any suitable level of automation (e.g., levels 0-5), as mentioned above.

[0038] Regardless of where the AV map data is stored and / or accessed, the AV map data may include geographic locations of known landmarks that are easily identifiable in the navigation environment in which the vehicle 100 is traveling. The locations of the landmarks may be generated by historical accumulation from other vehicles driving on the same road that collect data about the appearance and / or location of the landmarks (e.g., "crowdsourcing"). Thus, each landmark may be associated with a set of predetermined geographic coordinates that have been established. Thus, in addition to using location-based sensors (such as GNSS), the database of landmarks provided by the AV map data enables the vehicle 100 to identify landmarks using one or more image acquisition devices 104. Once identified, the vehicle 100 may implement other sensors (such as LIDAR, accelerometers, speedometers, etc.) or images from the image acquisition device 104 to assess the position and location of the vehicle 100 relative to the identified landmark location.

[0039] In addition, as mentioned above, the vehicle 100 can determine its own motion, which is referred to as "ego motion". Ego motion is commonly used in computer vision algorithms and other similar algorithms to represent the motion of the vehicle camera across multiple frames, which provides a baseline (i.e., spatial relationship) that can be used to calculate the 3D structure of the scene based on the corresponding images. The vehicle 100 can analyze the ego motion to determine the position and orientation of the vehicle 100 relative to the identified known landmarks. Because the landmarks are identified with predetermined geographic coordinates, the vehicle 100 can determine its position on the map based on: using the landmark-related geographic coordinates to determine its position relative to the identified landmark. Doing so provides a significant advantage, namely, combining the benefits of smaller-scale position tracking with the reliability of GNSS positioning systems while avoiding the shortcomings of both systems. It should be further noted that the analysis of ego motion in this manner is an example of an algorithm that can be implemented with monocular imaging to determine the relationship between the vehicle's orientation and the known orientation of one or more known landmarks, thereby assisting the vehicle in locating itself. However, ego motion is not necessary or relevant to other types of techniques, and is therefore not necessary for positioning using monocular imaging. Thus, in accordance with aspects as described herein, vehicle 100 may utilize any suitable type of positioning technology.

[0040] Thus, AV map data is typically constructed as part of a series of steps, which may involve any suitable number of vehicles that are selected to participate in the data collection process. For example, road segment data (RSD) is collected as part of the collection step. As each vehicle collects data, the data is classified into labeled data points, which are then sent to the cloud or another suitable external location. A suitable computing device (e.g., a cloud server) then analyzes the data points from various drives on the same road and aggregates and aligns these data points with each other. After the alignment has been performed, the data points are used to define the precise contours of the road infrastructure. Next, the relevant semantics that enable the vehicle to understand the immediate driving environment are identified, i.e., features and objects linked to the classified data points are defined. For example, the features and objects defined in this way may include traffic lights, road arrows, signs, road edges, drivable paths, lane splits, stop lines, lane markings, etc. of the driving environment, so that the vehicle can easily use the AV map data to identify these features and objects. This information is then compiled into a road book map, which constitutes a set of driving paths, semantic road information (such as features and objects), and aggregated driving behaviors.

[0041] For example, the map database 204, which may be stored as part of the one or more memories 202 or accessed via the computing system 150 and via the link 140, may include any suitable type of database configured to store (digital) map data of the vehicle 100, such as (digital) map data of the safety system 200. The one or more processors 102 may download information to the map database 204 via a wired or wireless data connection (e.g., one or more links 140) using a suitable communication network (e.g., via a cellular network and / or the Internet, etc.). Again, the map database 204 may store AV map data, which includes data related to the location of various landmarks (such as objects and other information items, including roads, water features, geographic features, businesses, points of interest, restaurants, gas stations, etc.) in a reference coordinate system.

[0042] Thus, the map database 204 may store not only the locations of such landmarks as part of the AV map data, but may also store descriptors associated with these landmarks, including, for example, names associated with any stored features, and may also store information related to the details of the project, such as the precise location and orientation of the project. In some cases, the AV map data may store a sparse data model that includes a polynomial representation of certain road features (e.g., lane markings) or a target trajectory for the vehicle 100. The AV map data may also include stored representations of various recognized landmarks that may be provided to determine or update the known position of the vehicle 100 relative to the target trajectory. The landmark representation may include data fields (such as landmark type, landmark location, etc.) and other possible identifiers. The AV map data may also include non-semantic features (including point clouds of certain objects or features in the environment) as well as feature points and descriptors.

[0043] The map database 204 may be augmented with data other than the AV map data, and / or the map database 204 and / or the AV map data may reside in part or in whole as part of the remote computing system 150. As discussed herein, the positions of known landmarks and the map database information that may be stored in the map database 204 and / or the remote computing system 150 may form what is referred to herein as "AV map data," "REM map data," or "road book map data." The one or more processors 102 may process sensory information (e.g., images, radar signals, depth information from LIDAR, or stereo processing of two or more images) and position information (such as GPS coordinates, ego motion of the vehicle, etc.) of the environment of the vehicle 100 to determine the current position, location, and / or orientation of the vehicle 100 relative to the known landmarks by using the information contained in the AV map. The determination of the position of the vehicle may thus be refined in this manner. Certain aspects of this technology may additionally or alternatively be included in localization technologies such as mapping and path planning models.

[0044] Safe driving model

[0045] In addition, the safety system 200 can implement a safe driving model or SDM (also referred to as a "driving strategy model," "driving strategy," or simply "driving model"), which can be used and / or executed as part of an ADAS system as discussed herein. For example, the safety system 200 may include a computer implementation of a formal model (such as a safe driving model) (e.g., as part of a driving strategy). The safe driving model may include an implementation of a mathematical model that formalizes an interpretation of applicable laws, standards, policies, etc. that apply to autonomous (e.g., ground) vehicles. In some embodiments, the SDM may include a standardized driving strategy, such as a Responsibility-Sensitive Safety (RSS) model. However, the embodiments are not limited to this particular example, and the SDM may be implemented using any suitable driving strategy model that defines various safety parameters that the AV should comply with to promote safe driving.

[0046] For example, the SDM can be designed to achieve, for example, three goals: first, the interpretation of the law should be reasonable, that is, the interpretation is consistent with the way humans interpret the law; second, the interpretation should produce useful driving strategies, meaning that the interpretation will produce flexible driving strategies rather than overly defensive driving, which will inevitably confuse other human drivers and will block traffic, and in turn limit the scalability of system deployment; and third, the interpretation should be efficiently verifiable, that is, it can be strictly proved that the autonomous driving (autonomous) vehicle correctly implements the interpretation of the law. The implementation of the safe driving model (e.g., vehicle 100) in the main vehicle can be or include the implementation of a mathematical model for safety assurance, which enables the identification and execution of correct responses to dangerous situations, thereby avoiding self-inflicted accidents.

[0047] The safe driving model may implement logic to apply driving behavior rules, such as the following five rules:

[0048] -Don't tailgate.

[0049] -Don't overtake recklessly.

[0050] - Yield the right of way rather than seize it.

[0051] -Be aware of areas of limited visibility.

[0052] -If you can avoid an accident without causing another one, you must do so.

[0053] It should be noted that these rules are not restrictive or exclusive, but can be amended in various aspects as necessary. Therefore, the rules represent a social driving "contract" that may vary from region to region and may also evolve over time. While these five rules currently apply to most countries, the rules may not be complete or identical in every region or country and may be amended.

[0054] As described above, the vehicle 100 may include also reference Figure 2The safety system 200 described herein. Thus, the safety system 200 can generate data to control or assist in controlling the ECU of the vehicle 100 and / or other components of the vehicle 100 to directly or indirectly navigate the vehicle 100 and / or control the driving operation of the vehicle, such navigation including driving the vehicle 100 or other suitable operations as further discussed herein. This navigation may optionally include adjusting one or more SDM parameters, which may occur in response to detecting any suitable type of feedback obtained via image processing, sensor measurements, etc. Feedback used for this purpose may be collectively referred to herein as "environmental data measurements" and include any suitable type of data identifying states associated with the external environment, vehicle occupants, the vehicle 100, and / or the cabin environment of the vehicle 100, etc.

[0055] For example, the environmental data measurements may be used to identify the longitudinal and / or lateral distances between the vehicle 100 and other vehicles, the presence of objects in the road, the location of hazards, etc. The environmental data measurements may be obtained via any suitable component of the vehicle 100 and / or may be the result of data analysis obtained via any suitable component of the vehicle, such as one or more image acquisition devices 104, one or more sensors 105, position sensors 106, speed sensors 108, one or more radar sensors 110, one or more LIDAR sensors 112, etc. To provide an illustrative example, the environmental data may be used to generate an environmental model based on any suitable combination of environmental data measurements. Thus, the vehicle 100 may utilize tasks performed via one or more trained models to perform various navigation-related operations within the framework of the driving strategy model.

[0056] For example, navigation-related operations may be performed by generating an environment model and using a driving strategy model in conjunction with the environment model to determine the action to be performed by the vehicle. That is, the driving strategy model may be applied based on the environment model to determine one or more actions (e.g., navigation-related operations) to be performed by the vehicle. The SDM may be used in conjunction with the driving strategy model (as part of an added layer or as an added layer) to ensure the safety of the action to be performed by the vehicle at any given moment. For example, the ADAS may utilize or refer to the SDM parameters defined by the safe driving model to determine the navigation-related operations of the vehicle 100 according to the environmental data measurement results according to a specific scenario. Therefore, the navigation-related operations may cause the vehicle 100 to perform specific actions based on the environment model to comply with the SDM parameters defined by the SDM model as discussed herein. For example, the navigation-related operations may include steering the vehicle 100, changing the acceleration and / or velocity of the vehicle 100, performing predetermined trajectory manipulation, etc. In other words, as described herein, an environment model may be generated at least in part based on sensor data received via various sensors of the vehicle 100, and the applicable driving strategy model may then be applied together with the environment model to determine the navigation-related operations to be performed by the vehicle.

[0057] Example Smart ADAS

[0058] The aspects discussed herein provide enhanced or intelligent ADAS functionality. For example, the on-board ADAS unit 290 may be implemented as one or more processors 102 and / or other suitable components of the safety system 200 as discussed above. For example, the ADAS unit 290 discussed herein (also referred to herein as ADAS290) may be implemented via one or more processors 214A, 214B, 216, and / or 218 of one or more processors 102, and may be implemented via any suitable computer-readable instructions stored in an associated memory (e.g., one or more memories 202) accessed by one or more processors 102, 214A, 214B, 216, 218, etc. (e.g., one or more processors) to perform any of the functions described herein.

[0059] According to aspects as discussed herein, the ADAS unit 290 can adjust the ADAS alert sensitivity in response to data received from a remote computing device (e.g., remote computing device 150) when one or more alert-based conditions have been met (e.g., proximity of an ADAS alert event, a route that the vehicle 100 navigates that will intersect an ADAS alert event, etc.). As further discussed herein, the sensitivity of the ADAS unit 290 can be adjusted based on changes in weather, other ADAS alert events that have been reported by other vehicle ADAS units. Additionally or alternatively, the ADAS unit 290 can adjust the ADAS alert sensitivity based on ADAS alert events collected from various sources (such as crowdsourcing, environmental condition sensing, road conditions, any suitable third-party geolocation services, etc.).

[0060] In order to achieve the adjustment of the ADAS alert sensitivity, it should be noted that the ADAS unit 290 operates according to a set of ADAS parameters, which together define the corresponding ADAS sensitivity configuration regarding how the ADAS unit 290 responds to meeting a specific alert-based condition. For example, the ADAS parameters may include an alert threshold time period identified by various types of detected ADAS alert events. Continuing with this example, the ADAS alert event may include a forward collision warning (FCW). Therefore, in this example, the FCW alert is displayed when an alert-based condition is met, and the alert-based condition includes that the estimated time of collision (TTC) between the vehicle 100 and another vehicle is less than a defined threshold time period, i.e., the alert threshold time period in this example. Therefore, when the alert threshold time period is adjusted according to the increased ADAS alert sensitivity configuration, the alert threshold time period increases, and the FCW alert will be displayed to the driver earlier, thereby enhancing safety.

[0061] As another example, ADAS parameters may alternatively define metrics other than time-based metrics, such as, for example, distance-based metrics. Thus, ADAS parameters may include an alert threshold distance identified using various types of detected ADAS alert events. Continuing with this example, ADAS alert events may include lane departure warnings (LDW). Thus, in this example, an LDW alert is displayed when an alert-based condition is met, the alert-based condition including the vehicle deviating from a lane marking without signaling a distance greater than a defined threshold deviation (i.e., an alert threshold distance). When the alert threshold distance is adjusted according to an increased sensitivity configuration so that the alert threshold distance is reduced, the LDW alert will be displayed to the driver earlier, thereby enhancing safety.

[0062] Although time and distance based ADAS parameters are provided above, these are provided as examples, and the embodiments described herein may be used to adjust the ADAS sensitivity configuration based on any suitable number and / or type of ADAS parameters, and may do so in response to satisfying any suitable number and type of conditions, as further discussed herein.

[0063] In any case, the smart ADAS unit 290 as discussed herein facilitates dynamic adjustment of various ADAS parameters based on remote data analysis of aggregated data sets. The aggregated data set may include data reported by multiple vehicle ADAS units over a large geographic area, and the data causes the generation of data messages sent to one or more vehicles present in a specific service area. Remote data analysis may include the generation of instructions according to a predetermined set of rules. The instructions included in the message sent to the vehicle indicate to each vehicle ADAS unit how to determine whether the sensitivity of the vehicle ADAS unit should be adjusted based on each ADAS alert event. The aggregated data set may be the result of crowdsourced data reported by other vehicles, which may also be used as a source for identifying ADAS alert events. When a message is received from a remote computing system, the local ADAS unit 290 of each vehicle may then selectively perform dynamic adjustment of the ADAS alert sensitivity setting. This may be performed, for example, by updating the ADAS parameters and / or adjusting how the ADAS unit 290 determines whether to present an ADAS alert as described above via the ADAS unit 290, which may be done in advance or even before the vehicle reaches a specific warning area.

[0064] Example Smart ADAS Architecture

[0065] Figure 3 An example architecture for implementing intelligent ADAS alerts according to one or more aspects of the present disclosure is presented. Figure 3As shown, the example architecture 300 includes any suitable number N of vehicles 302.1-302.N that communicate with the remote computing system 150 via a wireless infrastructure 304. Each of the vehicles 302.1-302.N can implement any suitable number and / or type of components, such as, for example, those discussed above with reference to the vehicle 100. Thus, each of the vehicles 302.1-302.N can include a safety system 200 and / or an ADAS unit 290, which can be configured to perform intelligent ADAS functions as further discussed herein. In addition, each of the vehicles 302.1-302.N can be configured to have different levels of ADAS and / or AV functions. For example, some of the vehicles 302.1-302.N can be configured to have sensors that are configured to enable the corresponding vehicle 302 to perform object detection, while other vehicles 302.1-302.N may not be configured to perform such functions. In this way, intelligent ADAS functionality as discussed in further detail herein may, in some cases, enhance or augment the functionality of a vehicle ADAS unit by providing the ADAS unit with information that might not otherwise be detected via the vehicle's onboard sensor suite.

[0066] In an embodiment, each of the vehicles 302.1-302.N may be configured to communicate with the remote computing system 150 via any suitable number and / or type of wireless infrastructure. Figure 3 304 is represented as wireless infrastructure 304. Thus, wireless infrastructure 304 may include a portion of a cellular or other suitable wireless network and include any suitable number of macrocells, femtocells, microcells, picocells, small cells, smart roadside infrastructure, edge computing systems and / or networks, etc. Wireless infrastructure 304 is communicatively coupled to a remote computing device via link 305, which may represent any suitable number and / or type of wired and / or wireless links, wires, telephone lines, relay hops, etc. Wireless infrastructure 304 is also communicatively coupled to each of vehicles 302.1-302.N via respective links 140.1-140.N, which may be identified with link 140 as described above. Thus, wireless infrastructure 304 is configured to enable each of vehicles 302.1-302.N to send data to and receive data from remote computing device 150, thereby facilitating two-way data communication between vehicles 302.1-302.N and remote computing device 150.

[0067] Again, the remote computing system 150 may be implemented according to any suitable architecture and / or network, and may constitute one or more physical computers, servers, processors, etc. that include such a system. In one embodiment, the remote computing system 150 may include any suitable type of memory, such as a non-transitory computer-readable medium, which may store computer-readable instructions that, when executed, enable the remote computing system 150 to perform intelligent ADAS-related functions as discussed herein. For example, the remote computing system 150 may execute an intelligent ADAS algorithm to generate a message sent to one or more of the vehicles 302.1-302.N via the corresponding links 104.1-104.N, indicating an adjusted ADAS alert sensitivity configuration. Thus, as further discussed herein, each vehicle 302.1-302.N may receive such a message and selectively adjust one or more ADAS parameters based on whether the ADAS alert event is related to the vehicle.

[0068] To this end, the remote computing system 150 may receive data messages sent by any suitable number of vehicles 302.1-302.N as they navigate within a coverage area, as well as any other suitable data source (e.g., such as smart infrastructure) that may be configured to monitor and report data regarding a particular driving environment. These data messages are alternatively referred to herein as data (e.g., first data) or vehicle ADAS messages. Note that although the term "vehicle ADAS message" is used herein, it should be understood that this is by way of example and not limitation, as the data sent to the remote computing system 150 as discussed herein may additionally or alternatively be sent via any suitable component (e.g., smart infrastructure) configured to collect and send such data. Furthermore, although Figure 3 Each of the vehicles 302.1-302.N is shown to be served by a single wireless infrastructure 304, but this is for ease of explanation, and it will be understood that the wireless infrastructure 304 may represent a plurality of cells or coverage areas, each of which is configured to receive data messages from a respective vehicle 302.1-302.N within a suitable range. In any case, the remote computing device 150 is configured to receive vehicle ADAS messages from any suitable number of vehicles 302.1-302.N or other suitable components that are currently within or have previously navigated the service area, which may be a predetermined size and / or shape.

[0069] Again, each of the vehicles 302.1-302.N can send a corresponding vehicle ADAS message according to any suitable type of communication protocol. The vehicle ADAS message can be sent, for example, via the wireless transceivers 208, 210, 212 of the safety system 200 (as discussed above) or via any suitable component of the corresponding vehicle 302.1-302.N. The vehicle messages can be sent continuously or according to any suitable periodic sending schedule (e.g., every 10 seconds, every 20 seconds, etc.). The periodicity of the sending schedule can be predetermined, configurable and / or conditional on any suitable type of relevant metric (e.g., such as the speed of the vehicle and / or the orientation of the vehicle). As an illustrative example, when the vehicle is in an urban or more dense environment, recognizing that there may be a greater incidence of detected ADAS events to be reported, the vehicle 302.1-302.N can increase its vehicle ADAS message sending frequency. Such a decision may include the ADAS unit 290 (or other suitable component, such as one or more processors 102) of the vehicle 100 utilizing, for example, geo-fencing technology to determine whether the vehicle 100 is in an area that triggers an adjustment in the frequency of sending vehicle ADAS messages. Additionally or alternatively, the vehicles 302.1-302.N may send their respective vehicle ADAS messages when a new ADAS event is detected and / or when an ADAS event matching a predetermined type is detected (e.g., when a traffic jam is detected, when a pedestrian is detected on a highway, etc.).

[0070] Regardless, the vehicle ADAS message may include any suitable amount of data, which may be included as part of an ADAS payload. The data included in the ADAS payload may be a function of the specific capabilities of the ADAS unit 290 of each vehicle 302.1-302.N. For example, a vehicle ADAS message may include a payload having data identifying the location of the vehicle sending the vehicle ADAS message. The location of the vehicle 302 may be represented by a geographic location (e.g., acquired via the GNSS of the vehicle 302) or alternatively by reference to the location of one or more AV map features. Additionally or alternatively, the vehicle ADAS message may include an ADAS payload that includes data identifying an ADAS alert event detected by the vehicle 302 sending the ADAS message and / or the current vehicle configuration. Further, the vehicle ADAS message may additionally or alternatively include aggregated road data that may identify the type of road on which the vehicle is traveling and / or a measure of road identification. For example, road data may be static in nature, indicating road type (e.g., paved, freeway, one-way street, etc.) and / or dynamic in nature (e.g., indicating changes in road conditions, road construction, etc.).

[0071] The remote computing system 150 is configured to aggregate data from any suitable number of data sources to form an aggregate data set. The remote computing system 150 is configured to use the aggregate data set according to a predetermined rule to indicate whether each vehicle should adjust the ADAS sensitivity setting when encountering the specific ADAS alarm event based on each ADAS alarm event. The aggregate data set may include, for example, data obtained via a vehicle ADAS message, which may contain any suitable type of information reported by each vehicle ADAS unit. For example, a vehicle ADAS message may identify an ADAS alarm event detected by each respective vehicle 302.1-302.N (or its ADAS unit), the position of each ADAS alarm event, and the current vehicle configuration. The current vehicle configuration may include, for example, the current ADAS configuration and sensitivity setting, and may additionally or alternatively include any other suitable information about the operation and capabilities of the vehicle and / or ADAS unit.

[0072] Additionally or alternatively, the remote computing system 150 may be configured to obtain data from any suitable number of data sources other than the vehicles 302.1-302.N. Data obtained from these additional data sources may also be included as part of the aggregate data set and may be used according to predetermined rules as described herein. These data sources may be used in a variety of ways. Figure 3 1 is represented as a supplemental data source 320, and may represent dynamically changing data provided from one or more suitable data sources such as a weather provider, a traffic data provider, a map data provider, etc. The remote computing system 150 may therefore access and process the aggregated data to determine, according to predetermined rules, whether the vehicle should adjust the ADAS sensitivity setting when encountering this particular ADAS alert event. In this way, the smart ADAS of the vehicle 100 enables other nearby vehicles (or other suitable road users) to change and increase the sensitivity of their ADAS alerts according to predetermined rules.

[0073] Thus, the remote computing system 150 uses the vehicle ADAS message to generate a message that is sent to one or more of the vehicles 302.1-302.N via the corresponding links 104.1-104.N, the message indicating the rules, conditions, and accompanying adjusted ADAS sensitivity configuration as described above. The message sent from the remote computing system 150 to one or more of the vehicles 302.1-302.N may alternatively be referred to herein as simply data (e.g., second data) or ADAS configuration messages. The properties of the vehicle ADAS messages and ADAS configuration messages will be discussed in further detail below.

[0074] ADAS alert events as discussed herein may include any suitable type of event that has been detected by the vehicle ADAS unit 290 and / or received, processed, and / or otherwise identified via the vehicle and / or the vehicle's ADAS unit 290. To provide some illustrative examples, an ADAS alert event may include general weather conditions at the vehicle's location, or may include weather alerts and / or more detailed weather-based conditions, such as visibility, precipitation, lightning, high winds, water on the road (e.g., puddles), fog, snow, icy conditions, and the like.

[0075] To provide some additional illustrative examples, ADAS warning events may include object-based ADAS warning events associated with various objects detected by the vehicle ADAS unit 290. Such object-based ADAS warning events may include, for example, detection of vulnerable road users (VRUs) such as pedestrians or cyclists. To provide further illustrative examples, ADAS warning events may include triggered ADAS warnings that have been issued by the vehicle's ADAS unit 290 in response to meeting certain conditions, such as detection of emergency braking, lane change warning (LCW), driver management system (DMS) detection of driver inattention, detection of forward collision warning (FCW), etc. Therefore, embodiments herein include utilizing any suitable type of event to identify ADAS warning events, which warning events may be detected by the vehicle 302.1-302.N via the vehicle's ADAS unit, the remote computing system 150 via the supplemental data source 150, or via any other suitable data source and / or component. ADASA warning events may include any suitable event that may have a safety impact on or otherwise be relevant to the safety of the vehicle and / or other road users, and / or be relevant to the issuance of future ADAS events by other vehicles sharing the same driving environment. Thus, an ADAS warning event that may be sent as part of a vehicle ADAS message may or may not trigger an ADAS warning to be issued by the vehicle sending the message.

[0076] In addition, the current ADAS configuration information that can be provided as part of the vehicle ADAS message can include any suitable type of information identified with the ADAS unit 290 of each respective vehicle 302.1-302.N. For example, this can include a set of ADAS parameters and / or capabilities of the ADAS unit 290, the type of warning that can be issued, vehicle information, driver information, etc. The current ADAS sensitivity setting that can be provided as part of the vehicle ADAS message in addition or alternatively can include information about the sensitivity configuration of the respective vehicle, which can indicate a threshold value based on the current time and / or distance identified with a corresponding alert-based condition, which alert-based condition causes an ADAS alert to be issued when met as described herein. As further discussed herein, subsequent ADAS configuration messages received via the remote computing device 150 cause the ADAS unit 290 to selectively adjust its ADAS parameters according to the adjusted ADAS alert sensitivity configuration. This can cause the ADAS unit 290 of the vehicle to issue subsequent ADAS alerts using the updated ADAS alert sensitivity configuration (e.g., as further discussed herein, if additional conditions are met).

[0077] To generate the ADAS configuration message, as described above, the remote computing device 150 receives the vehicle ADAS message sent by each of the vehicles 302.1-302.N in the service area, and can use the data contained in the ADAS payload of each vehicle ADAS alert message to generate an aggregate data set. In other words, the remote computing device 150 is configured to aggregate data received from one or more of the vehicles (via the transmitted vehicle ADAS message) and / or any data received via the supplemental data source 320 to generate an aggregate data set. For example, various types of supplemental data used in this manner can be set according to a predetermined configuration.

[0078] Figure 4A, an example of an aggregated data set 402 is shown, which includes ADAS warning events detected by one or more of the vehicles 302.1-302.N and / or determined via the remote computing system 150, and the reported geographic location of each of the detected ADAS warning events. It should be noted that, depending on the type of ADAS warning event, several instances of ADAS events can be identified from several vehicles in the same vicinity of each other, such as a threshold number of ADAS warning events reported by vehicles 302.1-302.N occupying an area with a predetermined size, shape, radius, etc. When these threshold conditions are met, the remote computing system 150 can therefore identify ADAS warning events at a specific location based on the location of each individual ADAS warning event reported by each vehicle ADAS unit. Examples of such ADAS warning events may include high traffic density, high pedestrian ratio, high emergency braking ratio (e.g., reporting multiple instances), etc.

[0079] In the event that multiple ADAS warning events are reported by multiple vehicles, the remote computing device 150 may use an average of the locations, a center location among the reported locations, select the road location closest to the reported ADAS warning event location, randomly select one of the ADAS warning events, or utilize any suitable calculation to identify the location of such ADAS warning event. Figure 4A As shown, the aggregate data set 402 also includes road data and weather information at the location of each ADAS alert event, which may also be derived from the transmitted vehicle ADAS messages and / or via the supplemental data sources 320 .

[0080] Thus, the aggregated dataset 402 may contain information that may be used to extract multiple ADAS warning events reported over a large service area, the warning events having been received from any suitable number of vehicles, and the aggregated dataset includes data indicating the type and location of the ADAS warning events within the service area. Figure 4A The illustrative examples shown may include more, less, or alternative information. Thus, the data contained in the aggregated dataset 402 may change over time as new ADAS alert events are reported, weather conditions change, vehicles move in and out of a service area, and the like. Once the aggregated dataset is generated in this manner, the remote computing system 150 uses the aggregated dataset 402 according to a set of predetermined rules that define a corresponding ADAS sensitivity configuration for each detected ADAS alert event and corresponding orientation. The application of this set of predetermined rules results in the generation of an ADAS configuration message, which is then sent to a particular vehicle 302.1-302.N and provides instructions to the vehicle regarding whether, when, and how to adjust the ADAS configuration settings.

[0081] In other words, the remote computing system 150 may execute a smart ADAS (SADAS) algorithm by processing an aggregate data set 402, which may include data sent via any suitable number of vehicles within a suitable service range, as well as other supplemental data sources 320 as discussed herein. Thus, the aggregate data set 402 may include any suitable type of data that may facilitate the generation of an ADAS configuration message and may identify or be used to identify an ADAS alert event. For example, the aggregate data 402 may include data sent by other vehicles, data regarding various "hot spots" (e.g., high density pedestrian or vehicle traffic), AV map data and / or any features derived from the AV map data, weather data, road data, etc., and corresponding locations identified with each data set.

[0082] In addition, the remote computing system 150 may optionally include in the sent ADAS configuration message a portion of the aggregated data set 402 that identifies the ADAS warning events to which the updated ADAS configuration settings may apply. For example, the portion of the aggregated data set 402 may be selected to identify the ADAS warning events and any other portion of the aggregated data set 402 for each ADAS warning event having an orientation within a threshold distance from the vehicle to which the ADAS configuration message is sent. In this way, the sent ADAS configuration message may indicate an ADAS sensitivity configuration to be potentially used by the vehicle's ADAS unit 290 based on the orientation of the vehicle. For example, the ADAS configuration message may identify the ADAS warning sensitivity configuration and the corresponding conditions (i.e., rule parameters to be satisfied) of the vehicle ADAS unit 290 to adjust its ADAS warning sensitivity configuration according to each ADAS warning event within a threshold distance of the vehicle receiving the ADAS configuration message.

[0083] It should be noted that, although referred to herein as a "configuration message", the ADAS configuration message does not need to indicate any parameters of the ADAS configuration sensitivity setting to be adjusted. Instead, the ADAS configuration message may represent a "smart" notification about a detected ADAS warning event, which has been detected by detecting data included in the aggregate data set 402. For example, the vehicle ADAS unit 290 may not be equipped to detect weather events, and therefore the remote computing system 150 may send an ADAS configuration message to a vehicle within a threshold distance of the detected weather event. Upon receiving such an ADAS configuration message, the ADAS unit 290 may cause the corresponding notification to be presented with or without further determining whether the warning event is related to the vehicle. In other words, in some embodiments, the remote computing system 150 may send an ADAS configuration message with a specific data payload that is identified by the receiving ADAS unit 290 of the vehicle as already related to the vehicle, and therefore in response, the ADAS unit 290 may display a notification based on the message content. In this way, the current ADAS capabilities of the vehicle can be supplemented via the use of the received ADAS configuration message.

[0084] Now turn to the use of predefined rule sets, such as Figure 4A The example rule set 450.1 shown may define rule parameters for any vehicle within a threshold distance of the location of an ADAS alert event 1 that take into account the vehicle's location and direction relative to the ADAS alert event, as well as road data at the location of the ADAS alert event. Figure 4A In the illustrative example shown, the rule condition provides that if the rule parameters are met, the recipient vehicle of the sent ADAS configuration message should generate a pedestrian warning when a pedestrian is detected at the same orientation (i.e., within a threshold distance of the orientation). Additionally or alternatively, the recipient vehicle should increase the ADAS sensitivity configuration to present a pedestrian warning earlier than the default scenario. Thus, in various embodiments, the predetermined rule 450.1 can indicate an update of either or both of these ADAS configuration settings for each ADAS alert event corresponding to a pedestrian detection in the aggregated data set 402.

[0085] That is, the corresponding sensitivity configuration is defined for each detected ADAS warning event in the detected ADAS warning event using the aggregated data set 402 according to the predetermined rule 450.1. The remote computing system 150 can generate an ADAS configuration message indicating that the ADAS configuration settings of the target vehicle are to be potentially adjusted. When the initial condition is met, for example, when the vehicle is within the threshold range of the orientation of the ADAS warning event, the parameters, results, corresponding ADAS configuration settings, etc. of the predefined rule 450.1 can be sent to each vehicle as part of the ADAS configuration message. The predefined rule 450.1 can also indicate which conditions should be met for the vehicle receiving the ADAS configuration message to adjust its ADAS warning sensitivity settings, and the new, updated settings that should be used. In this example, this occurs when the vehicle route (i.e., vector) intersects the orientation of the ADAS warning event, and the road data indicates that the speed limit of the road at the orientation is 110kph or higher. For example, the determination of whether the vehicle route intersects the location of the ADAS alert event may be additionally or alternatively conditioned on a threshold time period in which the vehicle (at the current route, heading, and speed) is expected to arrive at the location of the ADAS alert event. For example, when the ADAS alert event is expected to arrive within a time period less than a contact threshold time period (e.g., 20 seconds, 30 seconds, 1 minute, etc.), the vehicle ADAS unit 290 may determine that the ADAS configuration settings should be updated. In this way, rules may be customized for the specific capabilities of each vehicle.

[0086] Regardless, the remote computing system 150 may send a corresponding ADAS configuration message to each vehicle 302.1-302.N within a threshold distance of the ADAS alert event 1, within a predetermined geographic area of ​​the ADAS alert event 1, etc. For each vehicle receiving the message, each ADAS configuration message indicates a potential ADAS alert sensitivity configuration to be used by the vehicle. The potential ADAS alert sensitivity configuration may be implemented, for example, when a rule condition (and any other or alternative rules used by the vehicle) is met, thereby causing the vehicle ADAS unit 290 to adjust one or more parameters used according to the vehicle's current ADAS configuration settings.

[0087] In other words, in response to receiving the ADAS configuration message from the remote computing system 150, the ADAS unit 290 of each vehicle 302.1-302.N can locally determine whether the alert sensitivity of the ADAS unit 290 should be adjusted. In this illustrative example, when the vehicle route intersects the location of the ADAS alert event and the speed limit of the location is greater than or equal to 110kph, the predetermined rule set 450.1 instructs the ADAS unit 290 of the corresponding vehicle to perform the adjustment. If the vehicle ADAS unit 290 determines that these conditions are met, the vehicle ADAS unit 290 adjusts the current ADAS configuration settings to increase the ADAS alert sensitivity for the particular ADAS alert event when and if the event is encountered. Therefore, in this example, when a more dangerous condition (i.e., a higher speed limit) is detected, a pedestrian warning can be issued, while otherwise such an alert can be suppressed.

[0088] Additionally or alternatively, when the vehicle encounters an ADAS alert event, the increased ADAS alert sensitivity can cause a pedestrian detection warning to be issued faster than the default ADAS configuration setting would provide, i.e., when road speed is not taken into account. For example, when a pedestrian is detected, the initial (i.e., default) ADAS parameters can define alert-based conditions, such as a threshold time or distance as described above, which should be met to trigger the issuance of a pedestrian collision warning. Thus, the threshold time or distance can represent an example of an ADAS parameter used according to the current ADAS configuration setting of the vehicle. This parameter can be adjusted (e.g., by increasing) according to the adjusted alert sensitivity of the ADAS, thereby causing an ADAS alert to be issued earlier when the ADAS alert sensitivity is increased.

[0089] exist Figure 4A Another example of a predetermined rule set 450.2 is shown in . In this case, the aggregated data set 402 is used according to the rule set 450.2, which, as described above for the rule set 450.1, defines rule parameters that consider the location of the target vehicle within a threshold distance or geographic area of ​​the alarm event location. However, it should be noted that in this example, the rule set 450.2 also defines parameters that consider the weather at the current location of the vehicle. Therefore, the remote computing system 150 can specify in the sent ADAS configuration message that the recipient vehicle ADAS unit should increase ADAS sensitivity for any future ADAS alerts to be issued once the vehicle is located in an area experiencing fog or precipitation. The ADAS configuration message may optionally include this weather information for ADAS units that are unable to detect weather at their respective locations, or alternatively provide one or more geographic fences covering the current area experiencing fog, rain, snow, etc.

[0090] The vehicle receiving the ADAS configuration message can then identify whether the rule condition has been met by detecting current weather conditions, comparing its current location to the location and / or geo-fence included in the ADAS configuration message, etc. In any case, when the vehicle determines that the rule condition has been met, the increased ADAS alert sensitivity can cause a forward collision warning (FCW) to be presented earlier to compensate for the additional time required to stop the vehicle during steep conditions.

[0091] Continuing with this example, a FCW can be issued when the ADAS unit 290 determines that the calculated TTC value is less than a predetermined threshold time value. Therefore, in this example, the ADAS parameters used according to the current ADAS configuration settings of the vehicle include a predetermined threshold time value, and when the TTC value is less than the predetermined threshold time value, the alarm-based condition is met. To provide an illustrative example, the ADAS unit 290 can operate using three different levels of TTC sensitivity, each of which defines a different predetermined threshold time value. These can include a "near" level of 0.8 seconds before colliding with the vehicle in front, a "middle" level of 1.5 seconds, and a "far" level of 2.5 seconds. The ADAS unit 290 can default to using a medium level of 1.5 seconds as a predetermined threshold time value to operate. Then, upon receiving an ADAS configuration message and determining that the rule condition has been met, the ADAS unit 290 can change the ADAS alarm sensitivity level by increasing the ADAS parameter (i.e., the predetermined threshold time value) to a far level of 2.5 seconds, thereby allowing an FCW alarm to be issued earlier.

[0092] Figure 4B Another example of a predetermined rule set 450.3 is shown in FIG. 4. In this case, the aggregated data set 402 is used according to a rule parameter that considers the location of the target vehicle within a threshold distance or geographic area of ​​the alert event location 3, as described above for the rule set 450.1 for ADAS alert event location 1. In this example, the ADAS alert event location is identified using emergency braking reported at a specific geographic location. However, it should be noted that in this example, the rule parameters also consider the vehicle route, road speed limit data, and the weather at the ADAS alert event location.

[0093] Thus, the remote computing device 150 may specify in the transmitted ADAS configuration message that the recipient vehicle ADAS unit should increase ADAS sensitivity for any FCW alert issued at the location of ADAS event location 3, assuming the rule conditions are met. Thus, the ADAS alert sensitivity setting may be adjusted as described above so that a FCW is issued earlier when the vehicle determines that these conditions have been met. Thus, the set of predetermined rules 450.3 enhances safety by predicting the likelihood of generating a potential FCW alert at the same location where an emergency brake was previously reported.

[0094] Figure 4B Another example of a predetermined rule set 450.4 is further shown in FIG. 4. In this case, the aggregated data set 402 is used according to a rule parameter that considers the location of the target vehicle within a threshold distance or geographic area of ​​the warning event location 4, as described above for the rule set 450.1 for the ADAS warning event location 1. In this example, the rule parameters also consider the vehicle route and the weather at the ADAS warning event location. In this example, the ADAS warning event is identified using wheel slip reported at a specific geographic location.

[0095] In such Figure 4B In the illustrative example shown, the rule condition provides that the recipient vehicle of the transmitted ADAS configuration message should generate a slip condition warning upon determining that the rule condition has been met. Additionally or alternatively, the recipient vehicle should increase the ADAS alert sensitivity configuration to perform more stringent driver monitoring. Thus, in various embodiments, the predetermined rule 450.4 may update either or both of these ADAS configuration settings for each ADAS alert indication corresponding to wheel slip detection in the aggregated data set.

[0096] Again, the remote computing device 150 may send a corresponding ADAS configuration message to each vehicle 302.1-302.N within a threshold distance of an ADAS alert event 4, within a predetermined geographic area of ​​an ADAS alert event 4, etc. For each vehicle receiving the message, each ADAS configuration message indicates a potential ADAS alert sensitivity configuration to be used by that vehicle. Thus, when the rule conditions (and any other or alternative rules used by the vehicle) are met, the ADAS configuration settings may be adjusted.

[0097] In this illustrative example, the predetermined rule set 450.4 notifies the corresponding vehicle ADAS unit to perform the adjustment when the vehicle route intersects the location of the ADAS alert event and snow is present. Again, if the vehicle ADAS unit determines that these conditions are met, the vehicle ADAS unit adjusts the current ADAS configuration settings to increase the ADAS alert sensitivity for the specific ADAS alert event when and if an event is encountered. Therefore, in this example, when a slip condition is expected based on a previous ADAS alert generated at a specific location, the slip condition warning can be issued when it actually exists at the same location. Therefore, ADAS parameters that can be adjusted to increase the sensitivity of ADAS alerts can include a decision to issue a slip condition warning before wheel slip is detected. As another illustrative example, stricter driver monitoring can be achieved by increasing the sensitivity of the driver monitoring system (DMS), such as by increasing the frequency of driver monitoring, increasing time-based thresholds for driver gaze recognition using transfer, and the like.

[0098] Figure 5 Example road scenarios for adjusting ADAS alert sensitivity settings according to one or more aspects of the present disclosure are presented. Figure 5 Example scenario 500 is shown to demonstrate how ADAS alert sensitivity can be adjusted. Example scenario 500 includes a road with a T-shaped intersection, where vehicle 302.1 is traveling in a direction approaching the intersection. Figure 4A The locations of two of the ADAS warning events shown and discussed above are indicated at their respective locations on the road. Thus, the vehicle 302.1 may receive an ADAS configuration message as discussed herein that may indicate specific rule conditions to be satisfied in order for the ADAS unit 290 of the vehicle 302.1 to adjust its ADAS warning sensitivity setting for each of these detected ADAS warning events.

[0099] However, and as described above, the vehicle 302.1 need not adjust the ADAS alert sensitivity setting immediately upon receiving the ADAS configuration message, but may do so when other conditions are met to ensure that the detected ADAS alert event will be associated with (e.g., experienced by) the vehicle 302.1. In an embodiment, when the current route of the vehicle 302.1 intersects the orientation of the detected ADAS alert event, the ADAS unit 290 of the vehicle 302.1 may adjust the ADAS alert sensitivity setting as described herein. For example, if the vehicle 302.1 is following a planned route, the decision may be made based on the current route data. Additionally or alternatively, after the vehicle 302.1 completes a turn, a decision may be made by the ADAS unit 290 of the vehicle 302.1 as to whether to adjust the ADAS alert sensitivity setting as described herein.

[0100] For example, if Figure 5 302.1, the ADAS unit 290 of the vehicle 302.1 does not need to adjust the ADAS alert sensitivity setting for ADAS alert event 1, but can adjust the ADAS alert sensitivity setting for ADAS alert event 3. In this way, the ADAS unit 290 is able to display relevant alerts and change the sensitivity according to the configuration and predefined alert priorities, which can indicate adjustments only for those ADAS alert events that the vehicle 302.1 can experience based on the layout of its route, lanes and / or roads, the orientation of the ADAS alert events relative to the vehicle 302.1, etc. Therefore, the ADAS configuration message sent to the vehicle 302.1 can indicate the orientation and predefined rules for each of the ADAS alert events 1 and 3. However, in various embodiments, the "final" decision to actually adjust the ADAS alert sensitivity setting is calculated by the ADAS unit 290 of the vehicle 302.1 based on the driving direction and / or other relevant conditions.

[0101] Again, it should be noted that the decision about which ADAS warning events may be associated with a particular vehicle is also determined via the remote computing device, although the vehicle's ADAS unit 290 may perform the final determination in this regard. In other words, the remote computing system 150 may send an ADAS configuration message to a particular vehicle, which indicates a set of "candidate" ADAS warning events for each vehicle, which "candidate" ADAS warning events may be based on the vehicle's position and / or direction, road type, speed limit, etc. The ADAS unit 290 of each vehicle may then identify which of these candidate ADAS warning events is subsequently eligible to adjust the ADAS sensitivity setting when the vehicle will encounter an ADAS warning event. Thus, the remote computing system 150 may calculate a large number of ADAS warning events that may potentially apply to one or more vehicles traveling through the area over a large calculation radius. A subset of these ADAS warning events that are sent to each respective vehicle when each vehicle meets an initial set of predefined conditions may then be identified in the ADAS configuration message, the predefined conditions, for example, such as proximity to a subset of ADAS warning events (e.g., within a threshold distance, close to an ADAS warning event within a threshold time period, etc.).

[0102] Thus, the ADAS configuration message enables each ADAS unit 290 to adjust its ADAS alert sensitivity setting by adjusting the corresponding parameter of the ADAS corresponding to each different detected ADAS alert event. Thus, when the ADAS unit 290 determines that the ADAS alert event is related to the particular vehicle, any one of the different parameters of the ADAS unit 290 can be modified thereby to adjust the alert sensitivity of the ADAS for each of the detected ADAS alert events. Furthermore, it should be noted that embodiments are described herein primarily in terms of adjusting the ADAS sensitivity setting via a single parameter for each ADAS alert event, but this is for ease of explanation and is not intended to limit the functionality of the embodiments as described herein.

[0103] In contrast, embodiments as described herein may enable the ADAS unit 290 to adjust ADAS sensitivity settings by modifying any suitable number of parameters based on each ADAS alert event. To provide an illustrative example, upon detecting that a condition as defined by the predetermined rule set 450.2 is satisfied (a weather alert, e.g., a weather condition such as fog or precipitation at the vehicle's location), the vehicle's ADAS unit 290 may adjust the defined threshold time period for triggering FCW as described above, and may additionally adjust other ADAS parameters, such as increasing the threshold distance for triggering a headway monitoring and warning (HMW) ADAS alert, decreasing the threshold departure distance for triggering a lane departure warning (LDW) ADAS alert, etc.

[0104] Again, the remote computing system 150 can utilize the aggregated data set along with predefined rules to generate and send ADAS configuration messages to various vehicles, which can use these ADAS configuration messages to display notifications and / or adjust ADAS alert sensitivity settings. These predefined rule sets can utilize any suitable number and / or type of parameters in addition to or in place of the parameters such as FIG. 4A to FIG. 4B In this way, the predefined rule sets may provide flexibility as to how ADAS alerts are presented in various scenarios, which may take into account dynamically changing environmental and / or traffic conditions.

[0105] To provide another illustrative example, predefined rule set 450.3 utilizes the occurrence of an emergency braking ADAS warning event to identify. The rules may specify Figure 4B Other conditions not shown in the example above enable an intelligent determination to be made as to whether the ADAS alert event is particularly dangerous, which may result in an adjustment of the ADAS alert sensitivity setting by the vehicle ADAS unit. For example, if the location of ADAS alert event 3 is also identified as having a high traffic density, this may be considered as part of the rule parameters that cause the ADAS alert sensitivity setting to not be adjusted, as it will be appreciated that traffic typically brakes in response to this particular event. Conversely, if the location of ADAS alert event 3 is identified as having a low traffic density (and optionally the road speed limit is relatively high, such as 100kph or above), this may be considered as part of the rule parameters that cause the ADAS alert sensitivity setting to be adjusted. In this case, it will be appreciated that the sudden braking may be in response to a recent or unexpected event, which may also potentially be experienced by other vehicles, and therefore represents a higher safety risk than in the previous scenario.

[0106] As a further illustrative example, the predefined rule set may be based on a predefined profile established by a fleet manager using personal data collected in the context of a particular vehicle / driver. Thus, when used as part of a fleet management system, the vehicles served by the remote computing system 150 (i.e., the vehicles from which vehicle ADAS messages are received and to which ADAS configuration messages are sent) may be part of a fleet managed by a fleet operator. The predetermined rule set may include personalized parameters for a profile of a particular driver, who may be associated with an assigned vehicle. For example, by using the relevance of driver profile data to an assigned vehicle, a fleet manager may design a predefined rule set to be driver-specific by utilizing available driver data for that particular vehicle. As an illustrative example, a predefined rule sets different ADAS alert sensitivity settings between two different drivers that are adjusted in response to the same type of ADAS alert event. In this way, ADAS alerts may be provided to older drivers earlier than to younger drivers, anticipating differences in the reaction times of these drivers.

[0107] In this way, aspects as discussed herein implement a "smart" vehicle ADAS that can also act as an infrastructure component by providing data in the form of transmitted vehicle ADAS messages. In doing so, aspects described herein serve to extend safety features that may already exist in conventional ADAS units, such as headway monitoring and warning (HMW), forward collision warning (FCW), lane departure warning (LDW), etc. Examples of smart ADAS icons and their corresponding meanings, which can be presented as part of a smart ADAS embodiment as discussed herein, are shown in FIG. Figure 6 are provided by way of example and not limitation.

[0108] Figure 7 1 shows an example of a process flow according to one or more aspects of the present disclosure. Process flow 700 may include Figure 7 Alternative or additional steps not shown in the drawings and may be performed in a manner different from Figure 7 The steps are performed in the order shown.

[0109] refer to Figure 7, the process flow 700 can be a computer-implemented method executed by one or more processors (processing circuits) and / or storage devices and / or otherwise associated therewith. The functions associated with the process flow 700 as discussed herein can be performed, for example, via a suitable computing device and / or processing circuit identified by the vehicle 100 and / or the safety system 200, which can include a portion or all of the ADAS unit 290 as discussed herein. This can include, for example, one or more processors 102, one or more processors in the processors 214A, 214B, 216, 218, etc. that execute instructions stored in a suitable memory (e.g., one or more memories 202). In other aspects, the functions associated with the process flow 700 discussed herein can be performed, for example, via a processing circuit identified by any suitable type of computing device (e.g., chip, aftermarket product), which can be identified by the vehicle 100 or otherwise communicate with one or more components of the vehicle 100. The functions discussed with respect to the process flow 700 can be additionally or alternatively performed via one or more remote computing devices, such as the remote computing system 150 discussed herein.

[0110] The process flow 700 may begin with the transmission of first data to a remote computing device (block 702). As described herein, the first data may, for example, identify the location of a vehicle. Additionally or alternatively, the first data may include any other suitable information that may be used by the remote computing system 150 as discussed above to generate second data and send it to one or more vehicles within the service range. For example, the first data may include the location of a detected event, and the event may include an ADAS alert event or other detected event and / or an object identified by the ADAS unit 290, as well as a classification, description, type, etc. of the detected event. As an illustrative example, the first data may include the detection of pedestrians on a highway, and the location of the pedestrians when the pedestrians are detected. The first data may be included, for example, as part of a vehicle ADAS message that is periodically sent as described herein. The location of the vehicle may be identified, for example, via an onboard vehicle GPS system, via features identified in AV map data, etc. The remote computing device may be identified, for example, using the remote computing system 150 as discussed herein.

[0111] The process flow 700 may include receiving (block 704) second data from a remote computing device. As described herein, the second data may, for example, indicate an ADAS alarm sensitivity configuration. As described herein, the second data may include, for example, data as part of an ADAS configuration message that is sent to each vehicle 302.1-302.N and processed via the ADAS unit 290 of each vehicle. Thus, the second data may include predetermined rules and conditions to be satisfied for each identified ADAS alarm event, which the ADAS unit 290 may utilize to determine whether the ADAS alarm event is relevant to the vehicle. Additionally or alternatively, as described herein, the second data may include any other suitable information that may be used by the ADAS unit 290 of each vehicle to determine whether an ADAS alarm event identified with an ADAS alarm sensitivity configuration is relevant. For example, the second data may include data obtained via a supplementary data source 320, which may include third-party data such as weather data, traffic data, etc.

[0112] The process flow 700 may include determining (block 706) whether an ADAS warning event is associated with the vehicle, the ADAS warning event being identified using the ADAS sensitivity configuration received in the second data. Again, this determination may be made, for example, via the ADAS unit 290 of the vehicle, and may include determining whether any suitable number of conditions are met, such as the ADAS warning event location intersecting the vehicle's route, the vehicle approaching the ADAS warning event location within a threshold time period, weather conditions at the warning event location, etc.

[0113] If it is determined that the ADAS warning event is not related to the vehicle, the process flow 700 includes maintaining (block 708) the current ADAS warning sensitivity setting. Otherwise, the process flow 700 includes adjusting (block 710) the ADAS warning sensitivity setting by adjusting one or more parameters of the ADAS unit based on the ADAS configuration settings received in the second data. Again, these parameters may include, for example, various time-based and / or distance-based warning conditions (e.g., thresholds) that, when met, cause a specific type of ADAS warning to be issued for the ADAS warning event identified in the received second data.

[0114] Process flow 700 may include displaying (block 712) an ADAS alert based on the adjusted ADAS alert sensitivity configuration. For example, an ADAS alert may be displayed when an alert-based condition is met, which has been adjusted again based on the received ADAS configuration setting (block 710). Again, the ADAS alert may include various types of alerts issued based on the type of ADAS alert event detected, which may include, for example, the ADAS alerts described herein and Figure 6 Those alarms shown and discussed in .

[0115] Examples

[0116] The following examples relate to further aspects.

[0117] An example (e.g., Example 1) relates to a method. The method includes sending first data identifying the position of the vehicle to a remote computing device via a processing circuit of the vehicle; receiving second data from the remote computing device via the processing circuit, the second data indicating a sensitivity configuration to be used for an advanced driver assistance system (ADAS) of the vehicle based on the position of the vehicle; adjusting a parameter of the ADAS based on the received sensitivity configuration via the processing circuit, thereby adjusting the alert sensitivity of the ADAS; and causing an ADAS alert to be displayed via the processing circuit when an alert-based condition is satisfied according to the adjusted ADAS alert sensitivity of the ADAS.

[0118] Another example (eg, Example 2) relates to the previously described example (eg, Example 1), wherein the first data further identifies an ADAS warning event detected by the vehicle, a location of the ADAS warning event, and a current vehicle configuration.

[0119] Another instance (e.g., Example 3) involves a previously described instance (e.g., one or more of Examples 1-2), wherein second data sent to the vehicle is generated by a remote computing device by the following steps: aggregating first data received from multiple vehicles to generate an aggregated data set, the aggregated data set including (i) ADAS warning events detected by the multiple vehicles, and (ii) a corresponding location of each of the detected ADAS warning events; and using the aggregated data set to define a corresponding sensitivity configuration for each of the detected ADAS warning events according to a set of predetermined rules.

[0120] Another instance (e.g., Instance 4) relates to a previously described instance (e.g., one or more of Examples 1-3), wherein the second data further includes a portion of the aggregated data set corresponding to ADAS warning events having a location within a threshold distance from the vehicle.

[0121] Another instance (e.g., Instance 5) relates to a previously described instance (e.g., one or more of Instances 1-4), wherein adjusting parameters of the ADAS includes adjusting the sensitivity configuration of the vehicle's ADAS for one of the detected ADAS warning events when a current route of the vehicle intersects the bearing of one of the detected ADAS warning events.

[0122] Another instance (e.g., Instance 6) relates to a previously described instance (e.g., one or more of Instances 1-5), wherein adjusting the parameters of the ADAS includes: when the vehicle will reach the location of one of the detected ADAS warning events within a contact threshold time period, adjusting the sensitivity configuration of the vehicle's ADAS for the one of the detected ADAS warning events.

[0123] Another instance (e.g., Example 7) relates to a previously described instance (e.g., one or more of Examples 1-6), wherein the parameters of the adjusted ADAS include an alert threshold time period, and wherein the alert-based condition is met when the time required for the vehicle to reach a detected ADAS alert event is less than or equal to the alert threshold time period.

[0124] Another instance (e.g., Instance 8) relates to a previously described instance (e.g., one or more of Instances 1-7), wherein a set of predetermined rules defining corresponding sensitivity configurations for each of the detected ADAS warning events are further based on weather data and road data associated with each of the detected ADAS warning events.

[0125] Another instance (e.g., Example 9) relates to a previously described instance (e.g., one or more of Examples 1-8), wherein the second data indicates a sensitivity configuration for an ADAS to be used for the vehicle regarding a detected ADAS warning event, the warning event being included as part of the received second data.

[0126] Another instance (e.g., Instance 10) relates to a previously described instance (e.g., one or more of Instances 1-9), wherein a parameter of the ADAS is from a plurality of parameters, each of the plurality of parameters being identified by a different corresponding one of the detected ADAS warning events, and wherein adjusting the parameter of the ADAS includes adjusting each of the plurality of parameters of the ADAS to adjust the warning sensitivity of the ADAS for each of the detected ADAS warning events.

[0127] An example (e.g., Example 11) relates to a vehicle. The vehicle includes: a memory configured to store instructions; and a processing circuit that is part of an advanced driver assistance system (ADAS) of the vehicle, the processing circuit being configured to execute the instructions stored in the memory to cause the vehicle to: send first data identifying the position of the vehicle to a remote computing device; receive second data from the remote computing device, the second data indicating a sensitivity configuration of the ADAS to be used for the vehicle based on the position of the vehicle; adjust a parameter of the ADAS based on the received sensitivity configuration, thereby adjusting the alert sensitivity of the ADAS; and cause an ADAS alert to be displayed when an alert-based condition is satisfied according to the adjusted alert sensitivity of the ADAS.

[0128] Another example (eg, Example 12) relates to a previously described example (eg, Example 11), wherein the first data further identifies an ADAS warning event detected by the vehicle, a location of the ADAS warning event, and a current vehicle configuration.

[0129] Another instance (e.g., Example 13) relates to a previously described instance (e.g., one or more of Examples 11-12), wherein second data sent to the vehicle is generated by a remote computing device by the following steps: aggregating first data received from multiple vehicles to generate an aggregated data set, the aggregated data set including (i) ADAS warning events detected by the multiple vehicles, and (ii) a corresponding location of each of the detected ADAS warning events; and using the aggregated data set to define a corresponding sensitivity configuration for each of the detected ADAS warning events according to a set of predetermined rules.

[0130] Another instance (e.g., Instance 14) relates to a previously described instance (e.g., one or more of Instances 11-13), wherein the second data further includes a portion of the aggregated data set corresponding to ADAS warning events having a location within a threshold distance from the vehicle.

[0131] Another instance (e.g., Example 15) relates to a previously described instance (e.g., one or more of Examples 11-14), wherein the processing circuit is configured to adjust parameters of the ADAS by adjusting a sensitivity configuration of the vehicle's ADAS for one of the detected ADAS warning events when a current route of the vehicle intersects the bearing of one of the detected ADAS warning events.

[0132] Another instance (e.g., Example 16) relates to a previously described instance (e.g., one or more of Examples 11-15), wherein the processing circuit is configured to adjust parameters of the ADAS by adjusting the sensitivity configuration of the vehicle's ADAS for one of the detected ADAS warning events when the vehicle will reach the location of one of the detected ADAS warning events within a contact threshold time period.

[0133] Another instance (e.g., Instance 17) relates to a previously described instance (e.g., one or more of Instances 11-16), wherein the parameters of the adjusted ADAS include an alert threshold time period, and wherein the alert-based condition is met when the time required for the vehicle to reach a detected ADAS alert event is less than or equal to the alert threshold time period.

[0134] Another instance (e.g., Instance 18) relates to a previously described instance (e.g., one or more of Instances 11-17), wherein a set of predetermined rules defining corresponding sensitivity configurations for each of the detected ADAS warning events are further based on weather data and road data associated with each of the detected ADAS warning events.

[0135] Another instance (e.g., Instance 19) relates to a previously described instance (e.g., one or more of Instances 11-18), wherein the second data indicates a sensitivity configuration for an ADAS to be used for the vehicle regarding a detected ADAS warning event, the warning event being included as part of the received second data.

[0136] Another instance (e.g., Instance 20) relates to a previously described instance (e.g., one or more of Instances 11-19), wherein a parameter of the ADAS is from a plurality of parameters, each of the plurality of parameters being identified by a different corresponding one of the detected ADAS warning events, and wherein the processing circuit is configured to adjust the parameter of the ADAS by adjusting each of the plurality of parameters of the ADAS to adjust the warning sensitivity of the ADAS for each of the detected ADAS warning events.

[0137] One instance (e.g., instance 21) relates to a non-transitory computer-readable medium having instructions stored thereon that, when executed by processing circuitry associated with a vehicle, cause the vehicle to: send first data identifying a location of the vehicle to a remote computing device; receive second data from the remote computing device that indicates a sensitivity configuration for an advanced driver assistance system (ADAS) to be used for the vehicle based on the location of the vehicle; adjust a parameter of the ADAS based on the received sensitivity configuration, thereby adjusting an alert sensitivity of the ADAS; and cause an ADAS alert to be displayed when an alert-based condition is satisfied based on the adjusted alert sensitivity of the ADAS.

[0138] Another example (eg, Example 22) relates to a previously described example (eg, Example 21), wherein the first data further identifies an ADAS warning event detected by the vehicle, a location of the ADAS warning event, and a current vehicle configuration.

[0139] Another instance (e.g., Instance 23) relates to a previously described instance (e.g., one or more of Instances 21-22), wherein second data sent to the vehicle is generated by a remote computing device by the following steps: aggregating first data received from multiple vehicles to generate an aggregated data set, the aggregated data set including (i) ADAS warning events detected by the multiple vehicles, and (ii) a corresponding location of each of the detected ADAS warning events; and using the aggregated data set to define a corresponding sensitivity configuration for each of the detected ADAS warning events according to a set of predetermined rules.

[0140] Another instance (e.g., Instance 24) relates to a previously described instance (e.g., one or more of Instances 21-23), wherein the second data further includes a portion of the aggregated data set corresponding to ADAS warning events having a location within a threshold distance from the vehicle.

[0141] Another instance (e.g., Instance 25) relates to a previously described instance (e.g., one or more of Instances 21-24), wherein the instructions further cause the vehicle to adjust parameters of the ADAS by: adjusting the sensitivity configuration of the vehicle's ADAS for one of the detected ADAS warning events when the vehicle's current route intersects the location of one of the detected ADAS warning events.

[0142] Another instance (e.g., Instance 26) relates to a previously described instance (e.g., one or more of Instances 21-25), wherein the instructions further cause the vehicle to adjust parameters of the ADAS by adjusting a sensitivity configuration of the vehicle's ADAS for one of the detected ADAS warning events when the vehicle will reach the location of one of the detected ADAS warning events within a contact threshold time period.

[0143] Another instance (e.g., Instance 27) relates to a previously described instance (e.g., one or more of Instances 21-26), wherein the parameters of the adjusted ADAS include an alert threshold time period, and wherein the alert-based condition is met when the time required for the vehicle to reach a detected ADAS alert event is less than or equal to the alert threshold time period.

[0144] Another instance (e.g., Instance 28) relates to a previously described instance (e.g., one or more of Instances 21-27), wherein a set of predetermined rules defining corresponding sensitivity configurations for each of the detected ADAS warning events are further based on weather data and road data associated with each of the detected ADAS warning events.

[0145] Another instance (e.g., Instance 29) relates to a previously described instance (e.g., one or more of Instances 21-28), wherein the second data indicates a sensitivity configuration for an ADAS to be used for the vehicle regarding a detected ADAS warning event, the warning event being included as part of the received second data.

[0146] Another instance (e.g., instance 30) relates to a previously described instance (e.g., one or more of instances 21-29), wherein a parameter of the ADAS is from a plurality of parameters, each of the plurality of parameters being identified by a different corresponding one of the detected ADAS warning events, and wherein the instructions further cause the vehicle to adjust the parameter of the ADAS by adjusting each of the plurality of parameters of the ADAS to adjust the warning sensitivity of the ADAS for each of the detected ADAS warning events.

[0147] A method as shown and described.

[0148] An apparatus as shown and described.

[0149] in conclusion

[0150] The foregoing description of specific aspects will fully reveal the general nature of the present disclosure so that others can easily modify and / or adapt various applications of such specific aspects by applying knowledge within the art without undue experimentation and without departing from the general concept of the present disclosure. Therefore, based on the teachings and guidance presented herein, such adaptations and modifications are intended to be within the meaning and range of equivalents of the disclosed aspects. It should be understood that the phrases or terms herein are for the purpose of description rather than limitation, so that the terms or phrases of this specification are to be interpreted by those skilled in the art based on the teachings and guidance.

[0151] References in this specification to "one aspect", "an aspect", "exemplary aspects", etc. indicate that the described aspects may include certain features, structures, or characteristics, but not every aspect necessarily includes certain features, structures, or characteristics. In addition, such phrases do not necessarily refer to the same aspect. Further, when a certain feature, structure, or characteristic is described in conjunction with an aspect, whether or not explicitly described, it is considered to be within the knowledge of those skilled in the art to affect such feature, structure, or characteristic in conjunction with other aspects.

[0152] The exemplary aspects described herein are provided for illustrative purposes and not for limitation. Other exemplary aspects are possible, and the exemplary aspects may be modified. Therefore, this specification is not intended to limit the present disclosure. On the contrary, the scope of the present disclosure is defined only in accordance with the appended claims and their equivalents.

[0153] Aspects may be implemented in hardware (e.g., circuits), firmware, software, or any combination thereof. Aspects may also be implemented as instructions stored on a machine-readable medium that may be read and executed by one or more processors. A machine-readable medium may include any mechanism for storing or sending information in a form readable by a machine (e.g., a computing device). For example, a machine-readable medium may include a read-only memory (ROM); a random access memory (RAM); a disk storage medium; an optical storage medium; a flash memory device; an electrical, optical, acoustic, or other form of propagation signal (e.g., a carrier wave, an infrared signal, a digital signal, etc.), etc. Further, firmware, software, routines, instructions may be described herein as performing certain actions. However, it should be recognized that such descriptions are merely for convenience, and such actions are actually generated by a computing device, a processor, a controller, or other devices that execute firmware, software, routines, instructions, etc. Further, any implementation variant may be implemented by a general-purpose computer.

[0154] For the purposes of this discussion, the term "processing circuitry" or "processor circuitry" should be understood to mean one or more circuits, one or more processors, logic, or a combination thereof. For example, the circuitry may include analog circuitry, digital circuitry, state machine logic, other structural electronic hardware, or a combination thereof. The processor may include a microprocessor, a digital signal processor (DSP), or other hardware processor. The processor may be "hard-coded" with instructions to perform one or more corresponding functions in accordance with the various aspects described herein. Alternatively, the processor may access internal and / or external memory to retrieve instructions stored in the memory that, when executed by the processor, perform one or more corresponding functions associated with the processor and / or one or more functions and / or operations related to the operation of a component having a processor included therein.

[0155] In one or more of the exemplary aspects described herein, the processing circuitry may include a memory for storing data and / or instructions. The memory may be any well-known volatile and / or non-volatile memory, including, for example, a read-only memory (ROM), a random access memory (RAM), a flash memory, a magnetic storage medium, an optical disk, an erasable programmable read-only memory (EPROM), and a programmable read-only memory (PROM). The memory may be non-removable, removable, or a combination of both.

Claims

1. A method comprising: transmitting, via processing circuitry of the vehicle, first data identifying a location of the vehicle to a remote computing device; receiving, via the processing circuit, second data from the remote computing device, the second data indicating a sensitivity configuration to be used for an advanced driver assistance system (ADAS) of the vehicle based on the orientation of the vehicle; adjusting, via the processing circuit, a parameter of the ADAS based on the received sensitivity configuration, thereby adjusting an alert sensitivity of the ADAS; as well as An ADAS alert is caused to be displayed via the processing circuit when an alert-based condition is met according to the adjusted ADAS alert sensitivity of the ADAS. 2 . The method of claim 1 , wherein the first data further identifies an ADAS warning event detected by the vehicle, a location of the ADAS warning event, and a current vehicle configuration.

3. The method according to claim 1, wherein the second data sent to the vehicle is generated by the remote computing device by: aggregating first data received from a plurality of vehicles to generate an aggregated data set, the aggregated data set comprising (i) ADAS warning events detected by the plurality of vehicles, and (ii) a respective position of each of the detected ADAS warning events; and The aggregate data set is used to define a corresponding sensitivity configuration for each of the detected ADAS warning events according to a set of predetermined rules. 4 . The method of claim 3 , wherein the second data further comprises a portion of the aggregated data set corresponding to ADAS warning events having a location within a threshold distance from the vehicle.

5. The method of claim 4, wherein adjusting the parameter of the ADAS comprises: The sensitivity configuration of the ADAS of the vehicle is adjusted for one of the detected ADAS warning events when a current route of the vehicle intersects a location of the one of the detected ADAS warning events.

6. The method of claim 4, wherein adjusting the parameter of the ADAS comprises: When the vehicle will reach a location of one of the detected ADAS warning events within a contact threshold time period, the sensitivity configuration of the ADAS of the vehicle is adjusted for the one of the detected ADAS warning events.

7. The method of claim 1 , wherein the adjusted parameters of the ADAS include an alert threshold time period, and The alert-based condition is satisfied when the time required for the vehicle to reach the detected ADAS alert event is less than or equal to the alert threshold time period.

8. The method of claim 3, wherein the set of predetermined rules defining a corresponding sensitivity configuration for each of the detected ADAS warning events is further based on weather data and road data associated with each of the detected ADAS warning events.

9. The method according to claim 1, wherein: The second data indicates a sensitivity configuration to be used for the ADAS of the vehicle with respect to a detected ADAS warning event included as part of the received second data.

10. The method according to claim 9, wherein the parameter of the ADAS is from a plurality of parameters, each parameter of the plurality of parameters being identified by a different corresponding one of the detected ADAS warning events, and Wherein adjusting the parameters of the ADAS includes: Each of the plurality of parameters of the ADAS is adjusted to adjust the warning sensitivity of the ADAS for each of the detected ADAS warning events.

11. A vehicle comprising: a memory configured to store instructions; as well as a processing circuit that is part of an advanced driver assistance system (ADAS) of the vehicle, the processing circuit being configured to execute the instructions stored in the memory to cause the vehicle to: transmitting first data identifying a location of the vehicle to a remote computing device; receiving second data from the remote computing device, the second data indicating a sensitivity configuration of the ADAS to be used for the vehicle based on the orientation of the vehicle; adjusting a parameter of the ADAS based on the received sensitivity configuration, thereby adjusting the alert sensitivity of the ADAS; as well as When an alert-based condition is satisfied according to the adjusted alert sensitivity of the ADAS, an ADAS alert is caused to be displayed.

12. The vehicle of claim 11, wherein the first data further identifies an ADAS warning event detected by the vehicle, a location of the ADAS warning event, and a current vehicle configuration.

13. The vehicle of claim 11, wherein the second data transmitted to the vehicle is generated by the remote computing device by: aggregating first data received from a plurality of vehicles to generate an aggregated data set, the aggregated data set comprising (i) ADAS warning events detected by the plurality of vehicles, and (ii) a respective position of each of the detected ADAS warning events; and The aggregate data set is used to define a corresponding sensitivity configuration for each of the detected ADAS warning events according to a set of predetermined rules.

14. The vehicle of claim 13, wherein the second data further comprises a portion of the aggregate data set corresponding to ADAS warning events having a location within a threshold distance from the vehicle.

15. A vehicle according to claim 14, wherein the processing circuit is configured to adjust the parameters of the ADAS by: when the current route of the vehicle intersects the bearing of one of the detected ADAS warning events, adjusting the sensitivity configuration of the ADAS of the vehicle for the one of the detected ADAS warning events.

16. A vehicle according to claim 14, wherein the processing circuit is configured to adjust the parameters of the ADAS by: when the vehicle will reach the location of one of the detected ADAS warning events within a contact threshold time period, adjusting the sensitivity configuration of the ADAS of the vehicle for the one of the detected ADAS warning events.

17. The vehicle of claim 11, wherein the adjusted parameters of the ADAS include an alert threshold time period, and The alert-based condition is satisfied when the time required for the vehicle to reach the detected ADAS alert event is less than or equal to the alert threshold time period.

18. The vehicle of claim 13, wherein the set of predetermined rules defining corresponding sensitivity configurations for each of the detected ADAS warning events is further based on weather data and road data associated with each of the detected ADAS warning events.

19. The vehicle of claim 11, wherein: The second data indicates a sensitivity configuration to be used for the ADAS of the vehicle with respect to a detected ADAS warning event included as part of the received second data.

20. The vehicle according to claim 19, wherein the parameter of the ADAS is from a plurality of parameters, each parameter of the plurality of parameters being identified by a different corresponding one of the detected ADAS warning events, and Wherein the processing circuit is configured to adjust the parameter of the ADAS by adjusting each of the plurality of parameters of the ADAS to adjust the warning sensitivity of the ADAS for each of the detected ADAS warning events.

21. A non-transitory computer-readable medium having stored thereon instructions that, when executed by processing circuitry associated with a vehicle, cause the vehicle to: transmitting first data identifying a location of the vehicle to a remote computing device; receiving second data from the remote computing device, the second data indicating a sensitivity configuration to be used for an advanced driver assistance system (ADAS) of the vehicle based on the orientation of the vehicle; adjusting a parameter of the ADAS based on the received sensitivity configuration, thereby adjusting the alert sensitivity of the ADAS; as well as When an alert-based condition is satisfied according to the adjusted alert sensitivity of the ADAS, an ADAS alert is caused to be displayed.

22. The non-transitory computer-readable medium of claim 21, wherein the first data further identifies an ADAS warning event detected by the vehicle, a location of the ADAS warning event, and a current vehicle configuration.

23. The non-transitory computer-readable medium of claim 21 , wherein the second data sent to the vehicle is generated by the remote computing device by: aggregating first data received from a plurality of vehicles to generate an aggregated data set, the aggregated data set comprising (i) ADAS warning events detected by the plurality of vehicles, and (ii) a respective position of each of the detected ADAS warning events; and The aggregate data set is used to define a corresponding sensitivity configuration for each of the detected ADAS warning events according to a set of predetermined rules.

24. The non-transitory computer-readable medium of claim 23, wherein the second data further comprises a portion of the aggregate data set corresponding to ADAS warning events having a location within a threshold distance from the vehicle.

25. The non-transitory computer-readable medium of claim 24, wherein the instructions further cause the vehicle to adjust the parameters of the ADAS by adjusting the sensitivity configuration of the ADAS of the vehicle for one of the detected ADAS warning events when a current route of the vehicle intersects a location of the one of the detected ADAS warning events.

26. The non-transitory computer-readable medium of claim 24, wherein the instructions further cause the vehicle to adjust the parameters of the ADAS by adjusting the sensitivity configuration of the ADAS of the vehicle for one of the detected ADAS warning events when the vehicle will reach the location of the one of the detected ADAS warning events within a contact threshold time period.

27. The non-transitory computer readable medium of claim 21, wherein the adjusted parameter of the ADAS comprises an alert threshold time period, and The alert-based condition is satisfied when the time required for the vehicle to reach the detected ADAS alert event is less than or equal to the alert threshold time period.

28. The non-transitory computer-readable medium of claim 23, wherein the set of predetermined rules defining corresponding sensitivity configurations for each of the detected ADAS warning events is further based on weather data and road data associated with each of the detected ADAS warning events.

29. The non-transitory computer readable medium of claim 21, wherein: The second data indicates a sensitivity configuration to be used for the ADAS of the vehicle with respect to a detected ADAS warning event included as part of the received second data.

30. The non-transitory computer readable medium of claim 29, wherein the parameter of the ADAS is from a plurality of parameters, each parameter of the plurality of parameters being identified by a different corresponding one of the detected ADAS warning events, and Wherein the instructions further cause the vehicle to adjust the parameter of the ADAS by adjusting each of the plurality of parameters of the ADAS to adjust the warning sensitivity of the ADAS for each of the detected ADAS warning events.

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